I have a 2D masked array of values that I need to sort from lowest to highest. For example:
import numpy as np
# Make a random masked array
>>> ar = np.ma.array(np.round(np.random.normal(50, 10, 20), 1),
mask=np.random.binomial(1, .2, 20)).reshape((4,5))
>>> print(ar)
[[-- 51.9 38.3 46.8 43.3]
[52.3 65.0 51.2 46.5 --]
[56.7 51.1 -- 38.6 33.5]
[45.2 56.8 74.1 58.4 56.4]]
# Sort the array from lowest to highest, with a flattened index
>>> sorted_ind = ar.argsort(axis=None)
>>> print(sorted_ind)
[14 2 13 4 15 8 3 11 7 1 5 19 10 16 18 6 17 0 12 9]
But with the sorted indices, I need to divide them into two simple subsets: less than or equal to and greater than or equal to a given datum. Furthermore, I don't need the masked values, and they need to be removed. For example, with datum = 51.1, how do I filter down sorted_ind to the 10 indices above datum and 8 values below? (Note: there is one shared index due to the or equal to logic criteria. The 3 masked values can be removed from analysis). I need to preserve the flattened index position, as I use np.unravel_index(ind, ar.shape) later on.
to use where:
import numpy as np
np.random.seed(0)
# Make a random masked array
ar = np.ma.array(np.round(np.random.normal(50, 10, 20), 1),
mask=np.random.binomial(1, .2, 20)).reshape((4,5))
# Sort the array from lowest to highest, with a flattened index
sorted_ind = ar.argsort(axis=None)
tmp = ar.flatten()[sorted_ind]
print sorted_ind[np.ma.where(tmp<=51.0)]
print sorted_ind[np.ma.where(tmp>=51.0)]
but since tmp is sorted, you can use np.searchsorted():
tmp = ar.flatten()[sorted_ind].compressed() # compressed() will delete all invalid data.
idx = np.searchsorted(tmp, 51.0)
print sorted_ind[:idx]
print sorted_ind[idx:len(tmp)]